V.2.4

Allotrope Use Case

Heiner Oberkampf, PhD

June 29th

2018, Brussels

EC NMBP Workshop on a European Materials Ontology

Slide 2

Laboratory Analytical Process

sample dataanalytical process

Slide 3

Allotrope Structure 2018

Astrix Technology Group

BSSN Software

Elemental Machines

Erasmus MC

Fraunhofer IPA

The HDF Group

LabAnswer

LabWare

Mettler Toledo

NIST

SciBite

Stanford University

University of Illinois at Chicago

University of Southampton

More information: https://www.allotrope.org/

Slide 4

Allotrope Data Format (ADF)

HDF5

Platform Independent File Format

Allotrope Data Format (ADF): A Universal Data Container

Descriptive metadata about

• Method, instrument, sample,

process, result, etc.

• Provenance, audit trail

• Data Cube, Data Package

Analytical data represented by

one- or multidimensional arrays

of homogeneous data structures.

Analytical data represented by

arbitrary formats, incl. native

instrument formats, images,

pdf, video, etc.

Specifically designed to store

and organize large amounts

of scientific data.

Data Description

Semantic Graph Model

Data Cubes

Universal Data Container

Data Package

Virtual File System

APIs

(Java &

.N

ET class libraries)

Chromatogram 2D HDF

Slide 5

Data DescriptionSemantic Model

Data Cubes Universal Data Container

Data Package Virtual file system

Allotrope Data Format

Ontologies

Data Models

API

ADF Explorer

A standardised semantic model for data & metadata.

A set of constraints on the semantic model using data

shapes.

A high-performance binary data format.Instrument, vendor, platform agnostic.

An API to allow consistent creation & reading of ADF files.

ADF Explorer allows browsing of existing

ADF files.

Released July 2017Released Dec 2017

Coming in 2018

Standardized Data & Metadata

Slide 6

Semantics: Reference Ontologies, Constraints and Instance Data

Allotrope Data Format (ADF)

Instance Data

Allotrope Data Models (ADM)

Constraints

Allotrope Foundation Ontologies (AFO)

Classes & Properties

is structured by

is classified by

provide

standardized

vocabulary

• Standards based: RDF, OWL, SKOS

• Aligned with Basic Formal Ontology

• Modularization: ontology with taxonomy-backbones

• Reuse of standards: QUDT, ChEBI, Data Cube Ontology etc.

• Standards based implementation with W3C SHACL

• Specify usage of ontologies for experiment data

• Information about particular experiments

• Metadata for data cubes and data package

Slide 7

AFO: A Modularized Ontology based on Taxonomy Backbones

iao:information

content entity

obi:planned

process

bfo:material

entity

bfo:disposition

bfo:functionbfo:role

bfo:realizable entity

bfo:continuant bfo:occurrent

bfo:entity

bfo:processbfo:specifically dependent bfo:independentbfo:generically

dependent

bfo:quality

Top-Level Ontology

Binding

Quality Role & Function Result Material Equipment Process

has data output

Slide 8

AFO: A Modularized Ontology based on Taxonomy Backbones

iao:information

content entity

bfo:disposition

bfo:functionbfo:role

bfo:realizable entity

bfo:continuant bfo:occurrent

bfo:entity

bfo:processbfo:specifically dependent bfo:independentbfo:generically

dependent

bfo:quality

Top-Level Ontology

Binding

Quality Role & Function Result Material Equipment Process

balance

sample role

mass

trending plot

powderweighinghas material input

has role

obi:planned

process

bfo:material

entity

Slide 9

Ontology for HPLC Example

resultdevice

material

process

Slide 10

Benefits of Reusing Allotrope Data Format

Highly reusable, generic data container

Flexibility to integrate any custom taxonomy or

ontology

Can be reused for specification of other standards

Improves searchability & findability

Supports interoperability & reusability:

Makes data understandable

Removes ambiguities

Simplifies reproducibility

Significantly improves information exchange

within a community

Ready for productive use

E.g., data integrity, traceability,

audit-trail out-of-the-box

Prepares data for Big Data, Data Science, AI

applications

Data DescriptionSemantic Model

Data Cubes Universal Data Container

Data Package Virtual file system

Allotrope Data Format

Connecting data, people and organizations

Slide 12

Materials Module (Taxonomy Part)

Slide 13

Semantic Spectrum of Knowledge Organization Systems

• Deborah L. McGuinness. "Ontologies Come of Age". In Dieter Fensel, Jim Hendler, Henry Lieberman, and Wolfgang Wahlster, editors. Spinning the Semantic Web: Bringing the World Wide Web to Its Full Potential. MIT Press, 2003.

• Michael Uschold and Michael Gruninger “Ontologies and semantics for seamless connectivity” SIGMOD Rec. 33, 4 (December 2004), 58-64. DOI=http://dx.doi.org/10.1145/1041410.1041420

• Leo Obrst “The Ontology Spectrum”. Book section in of Roberto Poli, Michael Healy, Achilles Kameas “Theory and Applications of Ontology: Computer Applications”. Springer Netherlands, 17 Sep 2010.

• Leo Obrst and Mills Davis "Semantic Wave 2008 Report: Industry Roadmap to Web 3.0 & Multibillion Dollar Market Opportunities”. 2008.

Sources